Co-developing best bets for participatory disaster risk management in a postcolonial harmscape
Bibliographic record
Abstract
The conditions that characterize marginal and informal settlements in the Global South make the environmental hazards resulting from the current Climate Crisis more dangerous, giving rise to multifaceted risks that can be characterized as Anthropocene harmscapes. As such settlements are home to a large and growing population, this is an increasingly widespread problem that, if not addressed, could result in deaths, unrest and increasing numbers of climate refugees. Recognizing that neither climate change nor informality are going to disappear, it is essential to find practicable, contextualized and locally appropriate ways of mitigating and coping with climate change-exacerbated risks such as water scarcity, floods and fires. This paper describes a co-research process intended to enable residents of at-risk settlements to mobilize their own knowledges and experiences to identify and articulate strategies with a realistic potential for practical implementation. It demonstrates how this process yielded suggestions for actions that operate at a range of scales, from small changes to everyday practices that can be accomplished by individuals and households to infrastructural improvements that need cooperation and resourcing from local or national authorities. It also demonstrates some of the limitations of decolonial approaches that uniquely prioritize local knowledges when attempting to address challenges with global origins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".